Breast cancer is one of the most prevalent cancers in women from western countries. Detection and diagnosis of the disease is routinely done by X-ray mammography but its sensitivity varies significantly. Another common medical imaging technique is magnetic resonance imaging (MRI), which uses a powerful magnetic field to image the internal structure and certain functionality of a body. MRI is particularly suited for imaging soft tissue structures and is thus highly useful in the field of oncology for the detection of breast lesions.
A variety of techniques have been proposed to automatically segment breast lesions. One of the earlier techniques used temporal correlation of dynamic data to segment the malignant lesions. Sinha S, Lucas Quesada F A, DeBruhl N D, Sayre J, Farria D, Gorczyca D P, Bassett L W, “Multifeature analysis of Gd-enhanced MR images of breast lesions,” JMRI 1997; 7: 1016-1026. Lucas-Quesada et al. investigated semi-automated 2D-based methods. Lucas-Quesada F A, Sinha U, Sinha S., “Segmentation strategies for breast tumors from dynamic MR images,” JMRI 1996; 6: 753-763. Another approach proposed by Chen et al. used a semi-automated fuzzy c-means clustering based approach. Chen W, Giger M L, Bick U., “A fuzzy c-means (FCM)-based approach for computerized segmentation of breast lesions in dynamic contrast-enhanced MR images,” Acad Radiol 2006; 13: 63-72. One problem with these prior techniques is that they require too much user interaction. In addition, the output provided by these techniques is typically binary, and not applicable to different institutions' data.
Therefore, there is a need for a technology that mitigates or obviates the foregoing problems.